Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray');

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'));

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [15]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_inputs = tf.placeholder(tf.float32, 
                                 (None, image_width, image_height, image_channels),
                                'real_inputs')
    
    z_inputs = tf.placeholder(tf.float32,
                              (None, z_dim),
                              'z_inputs')
    
    learning_rate = tf.placeholder(tf.float32, 
                                   name='learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [40]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha = 0.1
        
        layer1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * layer1, layer1)
        
        layer2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(layer2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        
        layer3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='valid')
        bn3 = tf.layers.batch_normalization(layer3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
    
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [41]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.1
        
        
        x1 = tf.layers.dense(z, 4*4*512)
        
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.maximum(alpha * x1, x1)
        
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * x2, x2)
        
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = tf.maximum(alpha * x3, x3)
        
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        
        
        out = tf.tanh(logits)
        
    return out



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [18]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [19]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    d_updates = [opt for opt in update_ops if opt.name.startswith('discriminator')]
    g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]

    with tf.control_dependencies(d_updates):
        d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)

    with tf.control_dependencies(g_updates):
        g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
            
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [20]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [37]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    saver = tf.train.Saver(var_list=g_vars)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            batches = 0
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                resc_batch_images = 2*batch_images ### rescaling to match tanh output 
                
                train_d = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                train_g = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                train_gxtra = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                train_g2xtra = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})

                batches +=1
                
                if batches % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_real: batch_images, input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(batches),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)        

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [38]:
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 100... Discriminator Loss: 3.8727... Generator Loss: 0.0320
Epoch 1/2... Batch 200... Discriminator Loss: 2.9549... Generator Loss: 0.0696
Epoch 1/2... Batch 300... Discriminator Loss: 2.6681... Generator Loss: 0.0835
Epoch 1/2... Batch 400... Discriminator Loss: 1.6749... Generator Loss: 0.2667
Epoch 1/2... Batch 500... Discriminator Loss: 2.0300... Generator Loss: 0.1745
Epoch 1/2... Batch 600... Discriminator Loss: 2.0578... Generator Loss: 0.1601
Epoch 1/2... Batch 700... Discriminator Loss: 1.4731... Generator Loss: 0.3309
Epoch 1/2... Batch 800... Discriminator Loss: 2.1495... Generator Loss: 0.1359
Epoch 1/2... Batch 900... Discriminator Loss: 2.0245... Generator Loss: 0.1725
Epoch 1/2... Batch 1000... Discriminator Loss: 2.0486... Generator Loss: 0.1610
Epoch 1/2... Batch 1100... Discriminator Loss: 1.7154... Generator Loss: 0.8328
Epoch 1/2... Batch 1200... Discriminator Loss: 1.7124... Generator Loss: 0.2471
Epoch 1/2... Batch 1300... Discriminator Loss: 1.9023... Generator Loss: 0.1850
Epoch 1/2... Batch 1400... Discriminator Loss: 0.9717... Generator Loss: 0.6178
Epoch 1/2... Batch 1500... Discriminator Loss: 1.7235... Generator Loss: 0.2225
Epoch 1/2... Batch 1600... Discriminator Loss: 1.5119... Generator Loss: 0.2912
Epoch 1/2... Batch 1700... Discriminator Loss: 3.8844... Generator Loss: 0.0244
Epoch 1/2... Batch 1800... Discriminator Loss: 2.7029... Generator Loss: 0.0784
Epoch 2/2... Batch 100... Discriminator Loss: 2.2475... Generator Loss: 0.1447
Epoch 2/2... Batch 200... Discriminator Loss: 1.2808... Generator Loss: 0.3622
Epoch 2/2... Batch 300... Discriminator Loss: 0.5580... Generator Loss: 1.2859
Epoch 2/2... Batch 400... Discriminator Loss: 1.7953... Generator Loss: 1.1474
Epoch 2/2... Batch 500... Discriminator Loss: 1.0966... Generator Loss: 0.4990
Epoch 2/2... Batch 600... Discriminator Loss: 1.6064... Generator Loss: 0.2877
Epoch 2/2... Batch 700... Discriminator Loss: 0.8539... Generator Loss: 0.9173
Epoch 2/2... Batch 800... Discriminator Loss: 1.7053... Generator Loss: 0.3885
Epoch 2/2... Batch 900... Discriminator Loss: 1.5825... Generator Loss: 0.2650
Epoch 2/2... Batch 1000... Discriminator Loss: 1.7739... Generator Loss: 0.2206
Epoch 2/2... Batch 1100... Discriminator Loss: 1.1906... Generator Loss: 0.4803
Epoch 2/2... Batch 1200... Discriminator Loss: 0.6740... Generator Loss: 1.0035
Epoch 2/2... Batch 1300... Discriminator Loss: 0.2450... Generator Loss: 2.4666
Epoch 2/2... Batch 1400... Discriminator Loss: 0.6324... Generator Loss: 0.9602
Epoch 2/2... Batch 1500... Discriminator Loss: 1.5951... Generator Loss: 0.3004
Epoch 2/2... Batch 1600... Discriminator Loss: 1.5223... Generator Loss: 0.3205
Epoch 2/2... Batch 1700... Discriminator Loss: 1.8347... Generator Loss: 0.2745
Epoch 2/2... Batch 1800... Discriminator Loss: 2.4858... Generator Loss: 0.1094

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [39]:
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 100... Discriminator Loss: 4.0196... Generator Loss: 0.0246
Epoch 1/1... Batch 200... Discriminator Loss: 1.6820... Generator Loss: 0.4264
Epoch 1/1... Batch 300... Discriminator Loss: 1.9184... Generator Loss: 0.2837
Epoch 1/1... Batch 400... Discriminator Loss: 1.6948... Generator Loss: 0.4026
Epoch 1/1... Batch 500... Discriminator Loss: 2.3215... Generator Loss: 0.2905
Epoch 1/1... Batch 600... Discriminator Loss: 1.7375... Generator Loss: 0.4729
Epoch 1/1... Batch 700... Discriminator Loss: 1.9515... Generator Loss: 0.3319
Epoch 1/1... Batch 800... Discriminator Loss: 1.7104... Generator Loss: 0.4322
Epoch 1/1... Batch 900... Discriminator Loss: 2.1680... Generator Loss: 0.2807
Epoch 1/1... Batch 1000... Discriminator Loss: 1.8160... Generator Loss: 0.3580
Epoch 1/1... Batch 1100... Discriminator Loss: 1.8222... Generator Loss: 0.3748
Epoch 1/1... Batch 1200... Discriminator Loss: 1.6960... Generator Loss: 0.3955
Epoch 1/1... Batch 1300... Discriminator Loss: 2.0035... Generator Loss: 0.3403
Epoch 1/1... Batch 1400... Discriminator Loss: 1.6525... Generator Loss: 0.4398
Epoch 1/1... Batch 1500... Discriminator Loss: 1.7158... Generator Loss: 0.4708
Epoch 1/1... Batch 1600... Discriminator Loss: 1.8297... Generator Loss: 0.3524
Epoch 1/1... Batch 1700... Discriminator Loss: 1.6449... Generator Loss: 0.4725
Epoch 1/1... Batch 1800... Discriminator Loss: 1.6291... Generator Loss: 0.4454
Epoch 1/1... Batch 1900... Discriminator Loss: 1.7732... Generator Loss: 0.4340
Epoch 1/1... Batch 2000... Discriminator Loss: 2.1007... Generator Loss: 0.3225
Epoch 1/1... Batch 2100... Discriminator Loss: 1.7066... Generator Loss: 0.4269
Epoch 1/1... Batch 2200... Discriminator Loss: 1.8818... Generator Loss: 0.3484
Epoch 1/1... Batch 2300... Discriminator Loss: 1.7638... Generator Loss: 0.4349
Epoch 1/1... Batch 2400... Discriminator Loss: 1.6325... Generator Loss: 0.4506
Epoch 1/1... Batch 2500... Discriminator Loss: 1.8261... Generator Loss: 0.3949
Epoch 1/1... Batch 2600... Discriminator Loss: 1.9524... Generator Loss: 0.3724
Epoch 1/1... Batch 2700... Discriminator Loss: 1.5562... Generator Loss: 0.4692
Epoch 1/1... Batch 2800... Discriminator Loss: 1.6903... Generator Loss: 0.4477
Epoch 1/1... Batch 2900... Discriminator Loss: 1.6738... Generator Loss: 0.4263
Epoch 1/1... Batch 3000... Discriminator Loss: 1.6608... Generator Loss: 0.4424
Epoch 1/1... Batch 3100... Discriminator Loss: 1.7770... Generator Loss: 0.3926
Epoch 1/1... Batch 3200... Discriminator Loss: 1.5160... Generator Loss: 0.5309
Epoch 1/1... Batch 3300... Discriminator Loss: 1.7861... Generator Loss: 0.4221
Epoch 1/1... Batch 3400... Discriminator Loss: 1.6294... Generator Loss: 0.4381
Epoch 1/1... Batch 3500... Discriminator Loss: 1.8432... Generator Loss: 0.3649
Epoch 1/1... Batch 3600... Discriminator Loss: 1.7426... Generator Loss: 0.4197
Epoch 1/1... Batch 3700... Discriminator Loss: 1.8342... Generator Loss: 0.3738
Epoch 1/1... Batch 3800... Discriminator Loss: 1.6223... Generator Loss: 0.4268
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-39-6b62a2e2f9d4> in <module>()
     12 with tf.Graph().as_default():
     13     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 14           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-37-936b6ac18f30> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     29                 resc_batch_images = 2*batch_images ### rescaling to match tanh output
     30 
---> 31                 train_d = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
     32                 train_g = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
     33                 train_gxtra = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    776     try:
    777       result = self._run(None, fetches, feed_dict, options_ptr,
--> 778                          run_metadata_ptr)
    779       if run_metadata:
    780         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    980     if final_fetches or final_targets:
    981       results = self._do_run(handle, final_targets, final_fetches,
--> 982                              feed_dict_string, options, run_metadata)
    983     else:
    984       results = []

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1030     if handle is None:
   1031       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032                            target_list, options, run_metadata)
   1033     else:
   1034       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1037   def _do_call(self, fn, *args):
   1038     try:
-> 1039       return fn(*args)
   1040     except errors.OpError as e:
   1041       message = compat.as_text(e.message)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1019         return tf_session.TF_Run(session, options,
   1020                                  feed_dict, fetch_list, target_list,
-> 1021                                  status, run_metadata)
   1022 
   1023     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.